國立虎尾科技大學 |

Multi-objective, multi-class and multi-label data classification with class imbalance = theory and practices /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Multi-objective, multi-class and multi-label data classification with class imbalance/ by Sanjay Chakraborty, Lopamudra Dey.
Reminder of title:	theory and practices /
Author:	Chakraborty, Sanjay.
other author:	Dey, Lopamudra.
Published:	Singapore :Springer Nature Singapore : : 2024.,
Description:	xviii, 164 p. :ill. (chiefly color), digital ; : 24 cm.;
Contained By:	Springer Nature eBook
Subject:	Classification rule mining. -
Online resource:	https://doi.org/10.1007/978-981-97-9622-9
ISBN:	9789819796229

Multi-objective, multi-class and multi-label data classification with class imbalance = theory and practices /
Chakraborty, Sanjay.

Multi-objective, multi-class and multi-label data classification with class imbalancetheory and practices /[electronic resource] :by Sanjay Chakraborty, Lopamudra Dey. - Singapore :Springer Nature Singapore :2024. - xviii, 164 p. :ill. (chiefly color), digital ;24 cm. - Springer tracts in nature-inspired computing,2524-5538. - Springer tracts in nature-inspired computing..

1. Introduction to Classification -- 2. Class Imbalance and Data Irregularities in Classification -- 3. Multi-class Classification -- 4. Multi-Objective and Multi-Label Classification -- 5. Deep Learning Inspired Multiclass and Multilabel Classification -- 6. Applications of Multi-objective, Multi-label and Multi-class Classifications.

This book explores intricate world of data classification with 'Multi-Objective, Multi-Class, and Multi-Label Data Classification.' This book studies sophisticated methods and strategies for working with complicated data sets, tackling the difficulties of various classes, many objectives, and complicated labelling tasks. This resource fosters a deeper grasp of multi-dimensional data analysis in today's data-driven world by providing readers with the skills and insights needed to navigate the subtleties of modern classification jobs, from algorithmic techniques to practical applications.

ISBN: 9789819796229

Standard No.: 10.1007/978-981-97-9622-9doiSubjects--Topical Terms:

1112328
Classification rule mining.

LC Class. No.: QA76.9.D343

Dewey Class. No.: 006.312

Multi-objective, multi-class and multi-label data classification with class imbalance = theory and practices /
LDR:02091nam a2200337 a 4500 001 1153833
003 DE-He213
005 20241223115547.0
006 m d
007 cr nn 008maaau
008 250619s2024 si s 0 eng d
020 $a 9789819796229 $q (electronic bk.)
020 $a 9789819796212 $q (paper)
024 7 $a 10.1007/978-981-97-9622-9 $2 doi
035 $a 978-981-97-9622-9
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.9.D343
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.312 $2 23
090 $a QA76.9.D343 $b C435 2024
100 1 $a Chakraborty, Sanjay. $e author. $3 1403225
245 1 0 $a Multi-objective, multi-class and multi-label data classification with class imbalance $h [electronic resource] : $b theory and practices / $c by Sanjay Chakraborty, Lopamudra Dey.
260 $a Singapore : $c 2024. $b Springer Nature Singapore : $b Imprint: Springer,
300 $a xviii, 164 p. : $b ill. (chiefly color), digital ; $c 24 cm.
490 1 $a Springer tracts in nature-inspired computing, $x 2524-5538
505 0 $a 1. Introduction to Classification -- 2. Class Imbalance and Data Irregularities in Classification -- 3. Multi-class Classification -- 4. Multi-Objective and Multi-Label Classification -- 5. Deep Learning Inspired Multiclass and Multilabel Classification -- 6. Applications of Multi-objective, Multi-label and Multi-class Classifications.
520 $a This book explores intricate world of data classification with 'Multi-Objective, Multi-Class, and Multi-Label Data Classification.' This book studies sophisticated methods and strategies for working with complicated data sets, tackling the difficulties of various classes, many objectives, and complicated labelling tasks. This resource fosters a deeper grasp of multi-dimensional data analysis in today's data-driven world by providing readers with the skills and insights needed to navigate the subtleties of modern classification jobs, from algorithmic techniques to practical applications.
650 0 $a Classification rule mining. $3 1112328
650 1 4 $a Computational Intelligence. $3 768837
650 2 4 $a Artificial Intelligence. $3 646849
650 2 4 $a Machine Learning. $3 1137723
700 1 $a Dey, Lopamudra. $3 1481401
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
830 0 $a Springer tracts in nature-inspired computing. $3 1415223
856 4 0 $u https://doi.org/10.1007/978-981-97-9622-9
950 $a Intelligent Technologies and Robotics (SpringerNature-42732)